Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations1275
Missing cells176
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory853.3 B

Variable types

Categorical8
Text3
Numeric9
Boolean3

Alerts

Agirlik is highly overall correlated with IncHigh correlation
Birincil Depolama is highly overall correlated with Birincil Depolama TuruHigh correlation
Birincil Depolama Turu is highly overall correlated with Birincil DepolamaHigh correlation
Dokunmatik Ekran is highly overall correlated with Tur AdiHigh correlation
Ekran is highly overall correlated with Ekran Genisligi and 1 other fieldsHigh correlation
Ekran Genisligi is highly overall correlated with Ekran and 5 other fieldsHigh correlation
Ekran Yuksekligi is highly overall correlated with Ekran and 4 other fieldsHigh correlation
Grafik Karti Sirketi is highly overall correlated with Islemci SirketiHigh correlation
Ikincil Depolama is highly overall correlated with Ikincil Depolama TuruHigh correlation
Ikincil Depolama Turu is highly overall correlated with Ikincil DepolamaHigh correlation
Inc is highly overall correlated with AgirlikHigh correlation
Islemci Frekansi is highly overall correlated with Fiyat(Euro)High correlation
Islemci Sirketi is highly overall correlated with Grafik Karti SirketiHigh correlation
Isletim Sistemi is highly overall correlated with Retina Ekran and 1 other fieldsHigh correlation
Ram is highly overall correlated with Ekran Genisligi and 2 other fieldsHigh correlation
Retina Ekran is highly overall correlated with Ekran Genisligi and 3 other fieldsHigh correlation
Tur Adi is highly overall correlated with Dokunmatik EkranHigh correlation
Fiyat(Euro) is highly overall correlated with Ekran Genisligi and 3 other fieldsHigh correlation
Sirket is highly overall correlated with Ekran Genisligi and 2 other fieldsHigh correlation
Isletim Sistemi is highly imbalanced (65.6%) Imbalance
Retina Ekran is highly imbalanced (92.8%) Imbalance
Islemci Sirketi is highly imbalanced (82.1%) Imbalance
Ikincil Depolama Turu is highly imbalanced (66.0%) Imbalance
Ikincil Depolama has 1059 (83.1%) zeros Zeros

Reproduction

Analysis started2024-10-24 10:32:43.327337
Analysis finished2024-10-24 10:32:52.701396
Duration9.37 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Sirket
Categorical

High correlation 

Distinct27
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
Dell
291 
Lenovo
289 
HP
268 
Asus
152 
Acer
101 
Other values (22)
174 

Length

Max length153
Median length151
Mean length5.107451
Min length2

Characters and Unicode

Total characters6512
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.7%

Sample

1st rowApple
2nd rowApple
3rd rowHP
4th rowApple
5th rowApple

Common Values

ValueCountFrequency (%)
Dell 291
22.8%
Lenovo 289
22.7%
HP 268
21.0%
Asus 152
11.9%
Acer 101
 
7.9%
MSI 54
 
4.2%
Toshiba 48
 
3.8%
Apple 15
 
1.2%
Samsung 9
 
0.7%
Mediacom 7
 
0.5%
Other values (17) 41
 
3.2%

Length

2024-10-24T13:32:52.979160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dell 291
22.0%
lenovo 289
21.8%
hp 268
20.2%
asus 152
11.5%
acer 101
 
7.6%
msi 54
 
4.1%
toshiba 48
 
3.6%
apple 15
 
1.1%
samsung 9
 
0.7%
graphics 8
 
0.6%
Other values (36) 89
 
6.7%

Most occurring characters

ValueCountFrequency (%)
e 767
 
11.8%
o 729
 
11.2%
l 638
 
9.8%
s 398
 
6.1%
n 322
 
4.9%
D 303
 
4.7%
L 294
 
4.5%
v 289
 
4.4%
H 280
 
4.3%
A 276
 
4.2%
Other values (51) 2216
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 767
 
11.8%
o 729
 
11.2%
l 638
 
9.8%
s 398
 
6.1%
n 322
 
4.9%
D 303
 
4.7%
L 294
 
4.5%
v 289
 
4.4%
H 280
 
4.3%
A 276
 
4.2%
Other values (51) 2216
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 767
 
11.8%
o 729
 
11.2%
l 638
 
9.8%
s 398
 
6.1%
n 322
 
4.9%
D 303
 
4.7%
L 294
 
4.5%
v 289
 
4.4%
H 280
 
4.3%
A 276
 
4.2%
Other values (51) 2216
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 767
 
11.8%
o 729
 
11.2%
l 638
 
9.8%
s 398
 
6.1%
n 322
 
4.9%
D 303
 
4.7%
L 294
 
4.5%
v 289
 
4.4%
H 280
 
4.3%
A 276
 
4.2%
Other values (51) 2216
34.0%

Urun
Text

Distinct615
Distinct (%)48.5%
Missing8
Missing (%)0.6%
Memory size80.3 KiB
2024-10-24T13:32:53.250242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length42
Mean length15.524073
Min length6

Characters and Unicode

Total characters19669
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique442 ?
Unique (%)34.9%

Sample

1st rowMacBook Pro
2nd rowMacbook Air
3rd row250 G6
4th rowMacBook Pro
5th rowMacBook Pro
ValueCountFrequency (%)
inspiron 135
 
5.3%
ideapad 100
 
3.9%
thinkpad 99
 
3.9%
probook 72
 
2.8%
aspire 61
 
2.4%
elitebook 55
 
2.2%
latitude 52
 
2.1%
pro 42
 
1.7%
yoga 39
 
1.5%
13 39
 
1.5%
Other values (679) 1840
72.6%
2024-10-24T13:32:53.621073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1267
 
6.4%
0 1197
 
6.1%
o 1109
 
5.6%
5 1006
 
5.1%
1 876
 
4.5%
i 680
 
3.5%
e 645
 
3.3%
B 622
 
3.2%
7 620
 
3.2%
- 615
 
3.1%
Other values (57) 11032
56.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1267
 
6.4%
0 1197
 
6.1%
o 1109
 
5.6%
5 1006
 
5.1%
1 876
 
4.5%
i 680
 
3.5%
e 645
 
3.3%
B 622
 
3.2%
7 620
 
3.2%
- 615
 
3.1%
Other values (57) 11032
56.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1267
 
6.4%
0 1197
 
6.1%
o 1109
 
5.6%
5 1006
 
5.1%
1 876
 
4.5%
i 680
 
3.5%
e 645
 
3.3%
B 622
 
3.2%
7 620
 
3.2%
- 615
 
3.1%
Other values (57) 11032
56.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1267
 
6.4%
0 1197
 
6.1%
o 1109
 
5.6%
5 1006
 
5.1%
1 876
 
4.5%
i 680
 
3.5%
e 645
 
3.3%
B 622
 
3.2%
7 620
 
3.2%
- 615
 
3.1%
Other values (57) 11032
56.1%

Tur Adi
Categorical

High correlation 

Distinct6
Distinct (%)0.5%
Missing8
Missing (%)0.6%
Memory size72.1 KiB
Notebook
705 
Gaming
205 
Ultrabook
188 
2 in 1 Convertible
117 
Workstation
 
29

Length

Max length18
Median length8
Mean length8.7987372
Min length6

Characters and Unicode

Total characters11148
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUltrabook
2nd rowUltrabook
3rd rowNotebook
4th rowUltrabook
5th rowUltrabook

Common Values

ValueCountFrequency (%)
Notebook 705
55.3%
Gaming 205
 
16.1%
Ultrabook 188
 
14.7%
2 in 1 Convertible 117
 
9.2%
Workstation 29
 
2.3%
Netbook 23
 
1.8%
(Missing) 8
 
0.6%

Length

2024-10-24T13:32:53.743882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T13:32:53.892803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
notebook 705
43.6%
gaming 205
 
12.7%
ultrabook 188
 
11.6%
2 117
 
7.2%
in 117
 
7.2%
1 117
 
7.2%
convertible 117
 
7.2%
workstation 29
 
1.8%
netbook 23
 
1.4%

Most occurring characters

ValueCountFrequency (%)
o 2712
24.3%
t 1091
9.8%
b 1033
 
9.3%
e 962
 
8.6%
k 945
 
8.5%
N 728
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 422
 
3.8%
351
 
3.1%
Other values (12) 1968
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2712
24.3%
t 1091
9.8%
b 1033
 
9.3%
e 962
 
8.6%
k 945
 
8.5%
N 728
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 422
 
3.8%
351
 
3.1%
Other values (12) 1968
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2712
24.3%
t 1091
9.8%
b 1033
 
9.3%
e 962
 
8.6%
k 945
 
8.5%
N 728
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 422
 
3.8%
351
 
3.1%
Other values (12) 1968
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2712
24.3%
t 1091
9.8%
b 1033
 
9.3%
e 962
 
8.6%
k 945
 
8.5%
N 728
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 422
 
3.8%
351
 
3.1%
Other values (12) 1968
17.7%

Inc
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)1.3%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean15.036306
Minimum10.1
Maximum18.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:53.996880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile12.5
Q114
median15.6
Q315.6
95-th percentile17.3
Maximum18.4
Range8.3
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.4185495
Coefficient of variation (CV)0.094341621
Kurtosis-0.056133691
Mean15.036306
Median Absolute Deviation (MAD)0
Skewness-0.43423611
Sum19051
Variance2.0122827
MonotonicityNot monotonic
2024-10-24T13:32:54.083008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
15.6 645
50.6%
14 193
 
15.1%
17.3 164
 
12.9%
13.3 160
 
12.5%
12.5 39
 
3.1%
11.6 31
 
2.4%
13.5 6
 
0.5%
13.9 6
 
0.5%
12.3 5
 
0.4%
10.1 4
 
0.3%
Other values (7) 14
 
1.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
10.1 4
 
0.3%
11.3 1
 
0.1%
11.6 31
 
2.4%
12.3 5
 
0.4%
12.5 39
 
3.1%
13 2
 
0.2%
13.3 160
12.5%
13.5 6
 
0.5%
13.9 6
 
0.5%
14 193
15.1%
ValueCountFrequency (%)
18.4 1
 
0.1%
17.3 164
 
12.9%
17 1
 
0.1%
15.6 645
50.6%
15.4 4
 
0.3%
15 4
 
0.3%
14.1 1
 
0.1%
14 193
 
15.1%
13.9 6
 
0.5%
13.5 6
 
0.5%

Ram
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.7%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean8.4498816
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:54.177507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median8
Q38
95-th percentile16
Maximum64
Range62
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.1107457
Coefficient of variation (CV)0.60483045
Kurtosis15.310209
Mean8.4498816
Median Absolute Deviation (MAD)2
Skewness2.6909185
Sum10706
Variance26.119722
MonotonicityNot monotonic
2024-10-24T13:32:54.270705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 607
47.6%
4 365
28.6%
16 198
 
15.5%
6 35
 
2.7%
12 25
 
2.0%
32 17
 
1.3%
2 16
 
1.3%
24 3
 
0.2%
64 1
 
0.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
2 16
 
1.3%
4 365
28.6%
6 35
 
2.7%
8 607
47.6%
12 25
 
2.0%
16 198
 
15.5%
24 3
 
0.2%
32 17
 
1.3%
64 1
 
0.1%
ValueCountFrequency (%)
64 1
 
0.1%
32 17
 
1.3%
24 3
 
0.2%
16 198
 
15.5%
12 25
 
2.0%
8 607
47.6%
6 35
 
2.7%
4 365
28.6%
2 16
 
1.3%

Isletim Sistemi
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)0.7%
Missing8
Missing (%)0.6%
Memory size72.8 KiB
Windows 10
1046 
No OS
 
66
Linux
 
58
Windows 7
 
45
Chrome OS
 
27
Other values (4)
 
25

Length

Max length12
Median length10
Mean length9.4119968
Min length5

Characters and Unicode

Total characters11925
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmacOS
2nd rowmacOS
3rd rowNo OS
4th rowmacOS
5th rowmacOS

Common Values

ValueCountFrequency (%)
Windows 10 1046
82.0%
No OS 66
 
5.2%
Linux 58
 
4.5%
Windows 7 45
 
3.5%
Chrome OS 27
 
2.1%
macOS 11
 
0.9%
Windows 10 S 8
 
0.6%
Mac OS X 4
 
0.3%
Android 2
 
0.2%
(Missing) 8
 
0.6%

Length

2024-10-24T13:32:54.386778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T13:32:54.479671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
windows 1099
44.4%
10 1054
42.6%
os 97
 
3.9%
no 66
 
2.7%
linux 58
 
2.3%
7 45
 
1.8%
chrome 27
 
1.1%
macos 11
 
0.4%
s 8
 
0.3%
mac 4
 
0.2%
Other values (2) 6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1208
10.1%
o 1194
10.0%
n 1159
9.7%
i 1159
9.7%
d 1103
9.2%
W 1099
9.2%
w 1099
9.2%
s 1099
9.2%
1 1054
8.8%
0 1054
8.8%
Other values (17) 697
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1208
10.1%
o 1194
10.0%
n 1159
9.7%
i 1159
9.7%
d 1103
9.2%
W 1099
9.2%
w 1099
9.2%
s 1099
9.2%
1 1054
8.8%
0 1054
8.8%
Other values (17) 697
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1208
10.1%
o 1194
10.0%
n 1159
9.7%
i 1159
9.7%
d 1103
9.2%
W 1099
9.2%
w 1099
9.2%
s 1099
9.2%
1 1054
8.8%
0 1054
8.8%
Other values (17) 697
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1208
10.1%
o 1194
10.0%
n 1159
9.7%
i 1159
9.7%
d 1103
9.2%
W 1099
9.2%
w 1099
9.2%
s 1099
9.2%
1 1054
8.8%
0 1054
8.8%
Other values (17) 697
5.8%

Agirlik
Real number (ℝ)

High correlation 

Distinct169
Distinct (%)13.3%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.0460695
Minimum0.69
Maximum4.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:54.584709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.69
5-th percentile1.2
Q11.5
median2.04
Q32.31
95-th percentile3.2
Maximum4.7
Range4.01
Interquartile range (IQR)0.81

Descriptive statistics

Standard deviation0.66681028
Coefficient of variation (CV)0.32589816
Kurtosis2.4641772
Mean2.0460695
Median Absolute Deviation (MAD)0.39
Skewness1.1681227
Sum2592.37
Variance0.44463594
MonotonicityNot monotonic
2024-10-24T13:32:54.739112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 120
 
9.4%
2.1 58
 
4.5%
2 45
 
3.5%
2.4 42
 
3.3%
2.5 38
 
3.0%
2.3 37
 
2.9%
2.8 28
 
2.2%
1.86 25
 
2.0%
1.2 24
 
1.9%
1.4 24
 
1.9%
Other values (159) 826
64.8%
ValueCountFrequency (%)
0.69 4
 
0.3%
0.81 2
 
0.2%
0.91 1
 
0.1%
0.97 2
 
0.2%
0.98 2
 
0.2%
0.99 1
 
0.1%
1.05 7
0.5%
1.08 2
 
0.2%
1.09 2
 
0.2%
1.1 17
1.3%
ValueCountFrequency (%)
4.7 1
 
0.1%
4.6 4
 
0.3%
4.5 1
 
0.1%
4.42 11
0.9%
4.4 1
 
0.1%
4.36 4
 
0.3%
4.33 1
 
0.1%
4.3 4
 
0.3%
4.2 3
 
0.2%
4.14 3
 
0.2%

Fiyat(Euro)
Real number (ℝ)

High correlation 

Distinct785
Distinct (%)62.0%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1135.6845
Minimum174
Maximum6099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:54.873129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum174
5-th percentile326.2
Q1609
median985
Q31498
95-th percentile2449
Maximum6099
Range5925
Interquartile range (IQR)889

Descriptive statistics

Standard deviation701.95985
Coefficient of variation (CV)0.61809407
Kurtosis4.3263529
Mean1135.6845
Median Absolute Deviation (MAD)414
Skewness1.5128762
Sum1438912.3
Variance492747.63
MonotonicityNot monotonic
2024-10-24T13:32:54.978525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1799 14
 
1.1%
1099 14
 
1.1%
1499 12
 
0.9%
499 11
 
0.9%
1199 11
 
0.9%
1299 11
 
0.9%
999 10
 
0.8%
1399 9
 
0.7%
1349 9
 
0.7%
899 9
 
0.7%
Other values (775) 1157
90.7%
ValueCountFrequency (%)
174 1
0.1%
191.9 1
0.1%
196 1
0.1%
199 2
0.2%
202.9 1
0.1%
209 2
0.2%
210.8 1
0.1%
224 1
0.1%
229 2
0.2%
239 1
0.1%
ValueCountFrequency (%)
6099 1
0.1%
5499 1
0.1%
4899 1
0.1%
4389 1
0.1%
3975 1
0.1%
3949.4 1
0.1%
3890 1
0.1%
3659.4 1
0.1%
3588.8 1
0.1%
3499 1
0.1%

Ekran
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing8
Missing (%)0.6%
Memory size70.4 KiB
Full HD
833 
Standard
363 
4K Ultra HD
 
43
Quad HD+
 
28

Length

Max length11
Median length7
Mean length7.4443567
Min length7

Characters and Unicode

Total characters9432
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowFull HD
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Full HD 833
65.3%
Standard 363
28.5%
4K Ultra HD 43
 
3.4%
Quad HD+ 28
 
2.2%
(Missing) 8
 
0.6%

Length

2024-10-24T13:32:55.080276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T13:32:55.162474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hd 904
40.8%
full 833
37.6%
standard 363
16.4%
4k 43
 
1.9%
ultra 43
 
1.9%
quad 28
 
1.3%

Most occurring characters

ValueCountFrequency (%)
l 1709
18.1%
947
10.0%
H 904
9.6%
D 904
9.6%
u 861
9.1%
F 833
8.8%
a 797
8.4%
d 754
8.0%
t 406
 
4.3%
r 406
 
4.3%
Other values (7) 911
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1709
18.1%
947
10.0%
H 904
9.6%
D 904
9.6%
u 861
9.1%
F 833
8.8%
a 797
8.4%
d 754
8.0%
t 406
 
4.3%
r 406
 
4.3%
Other values (7) 911
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1709
18.1%
947
10.0%
H 904
9.6%
D 904
9.6%
u 861
9.1%
F 833
8.8%
a 797
8.4%
d 754
8.0%
t 406
 
4.3%
r 406
 
4.3%
Other values (7) 911
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1709
18.1%
947
10.0%
H 904
9.6%
D 904
9.6%
u 861
9.1%
F 833
8.8%
a 797
8.4%
d 754
8.0%
t 406
 
4.3%
r 406
 
4.3%
Other values (7) 911
9.7%

Ekran Genisligi
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.9%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1898.0994
Minimum1366
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:55.239278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1366
5-th percentile1366
Q11760
median1920
Q31920
95-th percentile3200
Maximum3840
Range2474
Interquartile range (IQR)160

Descriptive statistics

Standard deviation494.11607
Coefficient of variation (CV)0.26032149
Kurtosis6.5395094
Mean1898.0994
Median Absolute Deviation (MAD)0
Skewness2.2229757
Sum2404892
Variance244150.69
MonotonicityNot monotonic
2024-10-24T13:32:55.315135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1920 836
65.6%
1366 290
 
22.7%
3840 43
 
3.4%
2560 29
 
2.3%
3200 25
 
2.0%
1600 23
 
1.8%
2256 6
 
0.5%
1440 4
 
0.3%
2880 4
 
0.3%
2400 4
 
0.3%
Other values (2) 3
 
0.2%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
1366 290
 
22.7%
1440 4
 
0.3%
1600 23
 
1.8%
1920 836
65.6%
2160 2
 
0.2%
2256 6
 
0.5%
2400 4
 
0.3%
2560 29
 
2.3%
2736 1
 
0.1%
2880 4
 
0.3%
ValueCountFrequency (%)
3840 43
 
3.4%
3200 25
 
2.0%
2880 4
 
0.3%
2736 1
 
0.1%
2560 29
 
2.3%
2400 4
 
0.3%
2256 6
 
0.5%
2160 2
 
0.2%
1920 836
65.6%
1600 23
 
1.8%

Ekran Yuksekligi
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.8%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1072.161
Minimum768
Maximum2160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:55.387258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile768
Q1990
median1080
Q31080
95-th percentile1800
Maximum2160
Range1392
Interquartile range (IQR)90

Descriptive statistics

Standard deviation283.65658
Coefficient of variation (CV)0.26456528
Kurtosis5.8960272
Mean1072.161
Median Absolute Deviation (MAD)0
Skewness2.1447449
Sum1358428
Variance80461.055
MonotonicityNot monotonic
2024-10-24T13:32:55.464249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1080 831
65.2%
768 290
 
22.7%
2160 43
 
3.4%
1800 29
 
2.3%
900 27
 
2.1%
1440 25
 
2.0%
1600 10
 
0.8%
1504 6
 
0.5%
1200 5
 
0.4%
1824 1
 
0.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
768 290
 
22.7%
900 27
 
2.1%
1080 831
65.2%
1200 5
 
0.4%
1440 25
 
2.0%
1504 6
 
0.5%
1600 10
 
0.8%
1800 29
 
2.3%
1824 1
 
0.1%
2160 43
 
3.4%
ValueCountFrequency (%)
2160 43
 
3.4%
1824 1
 
0.1%
1800 29
 
2.3%
1600 10
 
0.8%
1504 6
 
0.5%
1440 25
 
2.0%
1200 5
 
0.4%
1080 831
65.2%
900 27
 
2.1%
768 290
 
22.7%

Dokunmatik Ekran
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing8
Missing (%)0.6%
Memory size2.6 KiB
False
1079 
True
188 
(Missing)
 
8
ValueCountFrequency (%)
False 1079
84.6%
True 188
 
14.7%
(Missing) 8
 
0.6%
2024-10-24T13:32:55.577055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

IPS Panel
Boolean

Distinct2
Distinct (%)0.2%
Missing8
Missing (%)0.6%
Memory size2.6 KiB
False
916 
True
351 
(Missing)
 
8
ValueCountFrequency (%)
False 916
71.8%
True 351
 
27.5%
(Missing) 8
 
0.6%
2024-10-24T13:32:55.651624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Retina Ekran
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing8
Missing (%)0.6%
Memory size2.6 KiB
False
1256 
True
 
11
(Missing)
 
8
ValueCountFrequency (%)
False 1256
98.5%
True 11
 
0.9%
(Missing) 8
 
0.6%
2024-10-24T13:32:55.744930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Islemci Sirketi
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing8
Missing (%)0.6%
Memory size67.3 KiB
Intel
1206 
AMD
 
60
Samsung
 
1

Length

Max length7
Median length5
Mean length4.9068666
Min length3

Characters and Unicode

Total characters6217
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowIntel
2nd rowIntel
3rd rowIntel
4th rowIntel
5th rowIntel

Common Values

ValueCountFrequency (%)
Intel 1206
94.6%
AMD 60
 
4.7%
Samsung 1
 
0.1%
(Missing) 8
 
0.6%

Length

2024-10-24T13:32:55.866405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T13:32:55.949897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intel 1206
95.2%
amd 60
 
4.7%
samsung 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 1207
19.4%
I 1206
19.4%
t 1206
19.4%
e 1206
19.4%
l 1206
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6217
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1207
19.4%
I 1206
19.4%
t 1206
19.4%
e 1206
19.4%
l 1206
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6217
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1207
19.4%
I 1206
19.4%
t 1206
19.4%
e 1206
19.4%
l 1206
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6217
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1207
19.4%
I 1206
19.4%
t 1206
19.4%
e 1206
19.4%
l 1206
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

Islemci Frekansi
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)2.0%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.3096448
Minimum0.9
Maximum3.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:56.048628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.2
Q12
median2.5
Q32.7
95-th percentile2.8
Maximum3.6
Range2.7
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.49827708
Coefficient of variation (CV)0.21573753
Kurtosis-0.077310873
Mean2.3096448
Median Absolute Deviation (MAD)0.2
Skewness-0.84965925
Sum2926.32
Variance0.24828005
MonotonicityNot monotonic
2024-10-24T13:32:56.142040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.5 285
22.4%
2.8 165
12.9%
2.7 164
12.9%
1.6 124
9.7%
2.3 86
 
6.7%
2 86
 
6.7%
1.8 78
 
6.1%
2.6 74
 
5.8%
1.1 51
 
4.0%
2.4 50
 
3.9%
Other values (15) 104
 
8.2%
ValueCountFrequency (%)
0.9 2
 
0.2%
1 1
 
0.1%
1.1 51
4.0%
1.2 12
 
0.9%
1.3 5
 
0.4%
1.44 10
 
0.8%
1.5 10
 
0.8%
1.6 124
9.7%
1.8 78
6.1%
1.9 2
 
0.2%
ValueCountFrequency (%)
3.6 5
 
0.4%
3.2 1
 
0.1%
3.1 3
 
0.2%
3 19
 
1.5%
2.9 19
 
1.5%
2.8 165
12.9%
2.7 164
12.9%
2.6 74
 
5.8%
2.5 285
22.4%
2.4 50
 
3.9%
Distinct90
Distinct (%)7.1%
Missing8
Missing (%)0.6%
Memory size78.4 KiB
2024-10-24T13:32:56.298056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length14.048145
Min length7

Characters and Unicode

Total characters17799
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)2.5%

Sample

1st rowCore i5
2nd rowCore i5
3rd rowCore i5 7200U
4th rowCore i7
5th rowCore i5
ValueCountFrequency (%)
core 1191
31.2%
i7 515
13.5%
i5 422
 
11.0%
7200u 193
 
5.0%
7700hq 147
 
3.8%
7500u 134
 
3.5%
i3 134
 
3.5%
6006u 81
 
2.1%
celeron 78
 
2.0%
8550u 73
 
1.9%
Other values (95) 854
22.3%
2024-10-24T13:32:56.534642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2555
14.4%
0 2149
12.1%
e 1497
8.4%
7 1364
 
7.7%
r 1324
 
7.4%
o 1285
 
7.2%
C 1270
 
7.1%
i 1155
 
6.5%
5 930
 
5.2%
U 790
 
4.4%
Other values (37) 3480
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2555
14.4%
0 2149
12.1%
e 1497
8.4%
7 1364
 
7.7%
r 1324
 
7.4%
o 1285
 
7.2%
C 1270
 
7.1%
i 1155
 
6.5%
5 930
 
5.2%
U 790
 
4.4%
Other values (37) 3480
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2555
14.4%
0 2149
12.1%
e 1497
8.4%
7 1364
 
7.7%
r 1324
 
7.4%
o 1285
 
7.2%
C 1270
 
7.1%
i 1155
 
6.5%
5 930
 
5.2%
U 790
 
4.4%
Other values (37) 3480
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2555
14.4%
0 2149
12.1%
e 1497
8.4%
7 1364
 
7.7%
r 1324
 
7.4%
o 1285
 
7.2%
C 1270
 
7.1%
i 1155
 
6.5%
5 930
 
5.2%
U 790
 
4.4%
Other values (37) 3480
19.6%

Birincil Depolama
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)1.0%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean445.40489
Minimum8
Maximum2048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:56.614523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile64
Q1256
median256
Q3512
95-th percentile1024
Maximum2048
Range2040
Interquartile range (IQR)256

Descriptive statistics

Standard deviation366.24806
Coefficient of variation (CV)0.82228117
Kurtosis3.0269792
Mean445.40489
Median Absolute Deviation (MAD)128
Skewness1.5884433
Sum564328
Variance134137.64
MonotonicityNot monotonic
2024-10-24T13:32:56.692115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
256 505
39.6%
1024 240
18.8%
128 175
 
13.7%
512 133
 
10.4%
500 124
 
9.7%
32 43
 
3.4%
2048 16
 
1.3%
64 13
 
1.0%
16 10
 
0.8%
180 5
 
0.4%
Other values (3) 3
 
0.2%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
8 1
 
0.1%
16 10
 
0.8%
32 43
 
3.4%
64 13
 
1.0%
128 175
 
13.7%
180 5
 
0.4%
240 1
 
0.1%
256 505
39.6%
500 124
 
9.7%
508 1
 
0.1%
ValueCountFrequency (%)
2048 16
 
1.3%
1024 240
18.8%
512 133
 
10.4%
508 1
 
0.1%
500 124
 
9.7%
256 505
39.6%
240 1
 
0.1%
180 5
 
0.4%
128 175
 
13.7%
64 13
 
1.0%

Ikincil Depolama
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.5%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean177.18074
Minimum0
Maximum2048
Zeros1059
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-24T13:32:56.763926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1024
Maximum2048
Range2048
Interquartile range (IQR)0

Descriptive statistics

Standard deviation417.03656
Coefficient of variation (CV)2.3537352
Kurtosis4.3653587
Mean177.18074
Median Absolute Deviation (MAD)0
Skewness2.2468592
Sum224488
Variance173919.49
MonotonicityNot monotonic
2024-10-24T13:32:56.827929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1059
83.1%
1024 187
 
14.7%
2048 15
 
1.2%
256 3
 
0.2%
500 2
 
0.2%
512 1
 
0.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
0 1059
83.1%
256 3
 
0.2%
500 2
 
0.2%
512 1
 
0.1%
1024 187
 
14.7%
2048 15
 
1.2%
ValueCountFrequency (%)
2048 15
 
1.2%
1024 187
 
14.7%
512 1
 
0.1%
500 2
 
0.2%
256 3
 
0.2%
0 1059
83.1%

Birincil Depolama Turu
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing8
Missing (%)0.6%
Memory size65.6 KiB
SSD
835 
HDD
359 
Flash Storage
 
65
Hybrid
 
8

Length

Max length13
Median length3
Mean length3.5319653
Min length3

Characters and Unicode

Total characters4475
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSD
2nd rowFlash Storage
3rd rowSSD
4th rowSSD
5th rowSSD

Common Values

ValueCountFrequency (%)
SSD 835
65.5%
HDD 359
28.2%
Flash Storage 65
 
5.1%
Hybrid 8
 
0.6%
(Missing) 8
 
0.6%

Length

2024-10-24T13:32:56.951628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T13:32:57.032028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ssd 835
62.7%
hdd 359
27.0%
flash 65
 
4.9%
storage 65
 
4.9%
hybrid 8
 
0.6%

Most occurring characters

ValueCountFrequency (%)
S 1735
38.8%
D 1553
34.7%
H 367
 
8.2%
a 130
 
2.9%
r 73
 
1.6%
e 65
 
1.5%
g 65
 
1.5%
o 65
 
1.5%
t 65
 
1.5%
65
 
1.5%
Other values (8) 292
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1735
38.8%
D 1553
34.7%
H 367
 
8.2%
a 130
 
2.9%
r 73
 
1.6%
e 65
 
1.5%
g 65
 
1.5%
o 65
 
1.5%
t 65
 
1.5%
65
 
1.5%
Other values (8) 292
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1735
38.8%
D 1553
34.7%
H 367
 
8.2%
a 130
 
2.9%
r 73
 
1.6%
e 65
 
1.5%
g 65
 
1.5%
o 65
 
1.5%
t 65
 
1.5%
65
 
1.5%
Other values (8) 292
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1735
38.8%
D 1553
34.7%
H 367
 
8.2%
a 130
 
2.9%
r 73
 
1.6%
e 65
 
1.5%
g 65
 
1.5%
o 65
 
1.5%
t 65
 
1.5%
65
 
1.5%
Other values (8) 292
 
6.5%

Ikincil Depolama Turu
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.3%
Missing8
Missing (%)0.6%
Memory size63.9 KiB
No
1059 
HDD
202 
SSD
 
4
Hybrid
 
2

Length

Max length6
Median length2
Mean length2.1689029
Min length2

Characters and Unicode

Total characters2748
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1059
83.1%
HDD 202
 
15.8%
SSD 4
 
0.3%
Hybrid 2
 
0.2%
(Missing) 8
 
0.6%

Length

2024-10-24T13:32:57.120275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T13:32:57.193708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 1059
83.6%
hdd 202
 
15.9%
ssd 4
 
0.3%
hybrid 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 1059
38.5%
o 1059
38.5%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1059
38.5%
o 1059
38.5%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1059
38.5%
o 1059
38.5%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1059
38.5%
o 1059
38.5%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

Grafik Karti Sirketi
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing8
Missing (%)0.6%
Memory size67.4 KiB
Intel
696 
Nvidia
396 
AMD
174 
ARM
 
1

Length

Max length6
Median length5
Mean length5.0363062
Min length3

Characters and Unicode

Total characters6381
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowIntel
2nd rowIntel
3rd rowIntel
4th rowAMD
5th rowIntel

Common Values

ValueCountFrequency (%)
Intel 696
54.6%
Nvidia 396
31.1%
AMD 174
 
13.6%
ARM 1
 
0.1%
(Missing) 8
 
0.6%

Length

2024-10-24T13:32:57.289401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T13:32:57.370104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intel 696
54.9%
nvidia 396
31.3%
amd 174
 
13.7%
arm 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 792
12.4%
I 696
10.9%
n 696
10.9%
t 696
10.9%
e 696
10.9%
l 696
10.9%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 792
12.4%
I 696
10.9%
n 696
10.9%
t 696
10.9%
e 696
10.9%
l 696
10.9%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 792
12.4%
I 696
10.9%
n 696
10.9%
t 696
10.9%
e 696
10.9%
l 696
10.9%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 792
12.4%
I 696
10.9%
n 696
10.9%
t 696
10.9%
e 696
10.9%
l 696
10.9%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%
Distinct109
Distinct (%)8.6%
Missing8
Missing (%)0.6%
Memory size79.2 KiB
2024-10-24T13:32:57.528413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length14.669298
Min length9

Characters and Unicode

Total characters18586
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)3.3%

Sample

1st rowIris Plus Graphics 640
2nd rowHD Graphics 6000
3rd rowHD Graphics 620
4th rowRadeon Pro 455
5th rowIris Plus Graphics 650
ValueCountFrequency (%)
graphics 707
19.8%
hd 613
17.2%
geforce 364
10.2%
620 349
9.8%
gtx 234
 
6.5%
520 199
 
5.6%
radeon 167
 
4.7%
1050 94
 
2.6%
uhd 68
 
1.9%
940mx 52
 
1.5%
Other values (90) 726
20.3%
2024-10-24T13:32:57.755124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2320
 
12.5%
0 1468
 
7.9%
G 1313
 
7.1%
r 1130
 
6.1%
c 1071
 
5.8%
a 906
 
4.9%
e 900
 
4.8%
i 758
 
4.1%
s 731
 
3.9%
p 707
 
3.8%
Other values (34) 7282
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2320
 
12.5%
0 1468
 
7.9%
G 1313
 
7.1%
r 1130
 
6.1%
c 1071
 
5.8%
a 906
 
4.9%
e 900
 
4.8%
i 758
 
4.1%
s 731
 
3.9%
p 707
 
3.8%
Other values (34) 7282
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2320
 
12.5%
0 1468
 
7.9%
G 1313
 
7.1%
r 1130
 
6.1%
c 1071
 
5.8%
a 906
 
4.9%
e 900
 
4.8%
i 758
 
4.1%
s 731
 
3.9%
p 707
 
3.8%
Other values (34) 7282
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2320
 
12.5%
0 1468
 
7.9%
G 1313
 
7.1%
r 1130
 
6.1%
c 1071
 
5.8%
a 906
 
4.9%
e 900
 
4.8%
i 758
 
4.1%
s 731
 
3.9%
p 707
 
3.8%
Other values (34) 7282
39.2%

Interactions

2024-10-24T13:32:50.780888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.408855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.049098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.749933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.647448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.624162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.468843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.255952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.988042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.854231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.471471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.106927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.853929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.741861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.688787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.588578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.328144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.067661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.927274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.540042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.174037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.945867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.827012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.757725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.699535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.408021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.154617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:51.009995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.620298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.244708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.082648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.914020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.849081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.791502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.494550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.239704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:51.070746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.684972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.323458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.187543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.222026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.928935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.880951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.569598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.320069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:51.176886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.751415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.394324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.266086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.325693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.020156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.953219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.648800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.395921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:51.249132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.814958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.489208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.386999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.404430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.129677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.024594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.722010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.516299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:51.332323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.906743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.560053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.484232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.483724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.229923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.098204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.789598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.601563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:51.437303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:44.990973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:45.649815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:46.577423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:47.558982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:48.337946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.180879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:49.898186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T13:32:50.686393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-24T13:32:57.854584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgirlikBirincil DepolamaBirincil Depolama TuruDokunmatik EkranEkranEkran GenisligiEkran YuksekligiGrafik Karti SirketiIPS PanelIkincil DepolamaIkincil Depolama TuruIncIslemci FrekansiIslemci SirketiIsletim SistemiRamRetina EkranTur AdiFiyat(Euro)Sirket
Agirlik1.0000.1880.2780.3650.190-0.078-0.0870.3850.2810.4570.3570.8780.3210.0960.1500.1930.0690.411-0.0210.218
Birincil Depolama0.1881.0000.5280.0830.205-0.044-0.0530.1110.129-0.3050.2000.2210.1000.0790.1800.0840.0000.189-0.0350.143
Birincil Depolama Turu0.2780.5281.0000.1520.2770.3070.3270.1610.2320.1700.1720.3640.3200.1110.3580.1640.0580.2850.3360.231
Dokunmatik Ekran0.3650.0830.1521.0000.3240.4090.4050.2660.1380.1480.1450.4260.2480.1070.2190.0850.0000.7760.2220.268
Ekran0.1900.2050.2770.3241.0000.9740.9490.2190.2700.1580.1530.2500.2740.0700.1720.2030.1390.2480.3670.209
Ekran Genisligi-0.078-0.0440.3070.4090.9741.0000.9960.2410.3470.1950.142-0.0840.3240.1360.3700.5500.7380.2540.6300.597
Ekran Yuksekligi-0.087-0.0530.3270.4050.9490.9961.0000.2580.3430.1910.147-0.0940.3130.1890.4040.5390.5190.2640.6190.403
Grafik Karti Sirketi0.3850.1110.1610.2660.2190.2410.2581.0000.1730.3020.2920.3710.3420.8090.1520.2120.0450.4140.2230.309
IPS Panel0.2810.1290.2320.1380.2700.3470.3430.1731.0000.1250.1150.2060.2110.0910.1850.1360.1390.3040.2740.315
Ikincil Depolama0.457-0.3050.1700.1480.1580.1950.1910.3020.1251.0000.7800.4190.3220.0000.0300.4070.0000.3430.3360.228
Ikincil Depolama Turu0.3570.2000.1720.1450.1530.1420.1470.2920.1150.7801.0000.2660.2670.0000.0550.2600.0000.3870.2380.269
Inc0.8780.2210.3640.4260.250-0.084-0.0940.3710.2060.4190.2661.0000.2840.1270.3150.1570.0980.459-0.0440.265
Islemci Frekansi0.3210.1000.3200.2480.2740.3240.3130.3420.2110.3220.2670.2841.0000.2250.1980.5000.2940.3230.5320.235
Islemci Sirketi0.0960.0790.1110.1070.0700.1360.1890.8090.0910.0000.0000.1270.2251.0000.1250.0000.0000.1070.1550.238
Isletim Sistemi0.1500.1800.3580.2190.1720.3700.4040.1520.1850.0300.0550.3150.1980.1251.0000.0000.8270.2360.1130.502
Ram0.1930.0840.1640.0850.2030.5500.5390.2120.1360.4070.2600.1570.5000.0000.0001.0000.0000.2220.7640.139
Retina Ekran0.0690.0000.0580.0000.1390.7380.5190.0450.1390.0000.0000.0980.2940.0000.8270.0001.0000.1900.1190.825
Tur Adi0.4110.1890.2850.7760.2480.2540.2640.4140.3040.3430.3870.4590.3230.1070.2360.2220.1901.0000.3110.302
Fiyat(Euro)-0.021-0.0350.3360.2220.3670.6300.6190.2230.2740.3360.238-0.0440.5320.1550.1130.7640.1190.3111.0000.214
Sirket0.2180.1430.2310.2680.2090.5970.4030.3090.3150.2280.2690.2650.2350.2380.5020.1390.8250.3020.2141.000

Missing values

2024-10-24T13:32:51.575204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-24T13:32:51.843323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-24T13:32:52.424093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SirketUrunTur AdiIncRamIsletim SistemiAgirlikFiyat(Euro)EkranEkran GenisligiEkran YuksekligiDokunmatik EkranIPS PanelRetina EkranIslemci SirketiIslemci FrekansiIslemci ModeliBirincil DepolamaIkincil DepolamaBirincil Depolama TuruIkincil Depolama TuruGrafik Karti SirketiGrafik Karti Modeli
0AppleMacBook ProUltrabook13.38.0macOS1.371339.69Standard2560.01600.0NoYesYesIntel2.3Core i5128.00.0SSDNoIntelIris Plus Graphics 640
1AppleMacbook AirUltrabook13.38.0macOS1.34898.94Standard1440.0900.0NoNoNoIntel1.8Core i5128.00.0Flash StorageNoIntelHD Graphics 6000
2HP250 G6Notebook15.68.0No OS1.86575.00Full HD1920.01080.0NoNoNoIntel2.5Core i5 7200U256.00.0SSDNoIntelHD Graphics 620
3AppleMacBook ProUltrabook15.416.0macOS1.832537.45Standard2880.01800.0NoYesYesIntel2.7Core i7512.00.0SSDNoAMDRadeon Pro 455
4AppleMacBook ProUltrabook13.38.0macOS1.371803.60Standard2560.01600.0NoYesYesIntel3.1Core i5256.00.0SSDNoIntelIris Plus Graphics 650
5AcerAspire 3Notebook15.64.0Windows 102.10400.00Standard1366.0768.0NoNoNoAMD3.0A9-Series 9420500.00.0HDDNoAMDRadeon R5
6AppleMacBook ProUltrabook15.416.0Mac OS X2.042139.97Standard2880.01800.0NoYesYesIntel2.2Core i7256.00.0Flash StorageNoIntelIris Pro Graphics
7AppleMacbook AirUltrabook13.38.0macOS1.341158.70Standard1440.0900.0NoNoNoIntel1.8Core i5256.00.0Flash StorageNoIntelHD Graphics 6000
8AsusZenBook UX430UNUltrabook14.016.0Windows 101.301495.00Full HD1920.01080.0NoNoNoIntel1.8Core i7 8550U512.00.0SSDNoNvidiaGeForce MX150
9AcerSwift 3Ultrabook14.08.0Windows 101.60770.00Full HD1920.01080.0NoYesNoIntel1.6Core i5 8250U256.00.0SSDNoIntelUHD Graphics 620
SirketUrunTur AdiIncRamIsletim SistemiAgirlikFiyat(Euro)EkranEkran GenisligiEkran YuksekligiDokunmatik EkranIPS PanelRetina EkranIslemci SirketiIslemci FrekansiIslemci ModeliBirincil DepolamaIkincil DepolamaBirincil Depolama TuruIkincil Depolama TuruGrafik Karti SirketiGrafik Karti Modeli
1265LenovoIdeaPad Y700-15ISKNotebook15.68.0Windows 102.60899.00Full HD1920.01080.0NoYesNoIntel2.6Core i7 6700HQ1024.00.0HDDNoNvidiaGeForce GTX 960M
1266HPPavilion 15-AW003nvNotebook15.66.0Windows 102.04549.99Full HD1920.01080.0NoNoNoAMD2.9A9-Series 94101024.00.0HybridNoAMDRadeon R7 M440
1267DellInspiron 3567Notebook15.68.0Linux2.30805.99Standard1366.0768.0NoNoNoIntel2.7Core i7 7500U1024.00.0HDDNoAMDRadeon R5 M430
1268HPStream 11-Y000naNetbook11.62.0Windows 101.17209.00Standard1366.0768.0NoNoNoIntel1.6Celeron Dual Core N306032.00.0Flash StorageNoIntelHD Graphics 400
1269AsusX556UJ-XO044T (i7-6500U/4GB/500GB/GeForceNotebook15.64.0Windows 102.20720.32Standard1366.0768.0NoNoNoIntel2.5Core i7 6500U500.00.0HDDNoNvidiaGeForce 920M
1270LenovoYoga 500-14ISK2 in 1 Convertible14.04.0Windows 101.80638.00Full HD1920.01080.0YesYesNoIntel2.5Core i7 6500U128.00.0SSDNoIntelHD Graphics 520
1271LenovoYoga 900-13ISK2 in 1 Convertible13.316.0Windows 101.301499.00Quad HD+3200.01800.0YesYesNoIntel2.5Core i7 6500U512.00.0SSDNoIntelHD Graphics 520
1272LenovoIdeaPad 100S-14IBRNotebook14.02.0Windows 101.50229.00Standard1366.0768.0NoNoNoIntel1.6Celeron Dual Core N305064.00.0Flash StorageNoIntelHD Graphics
1273HP15-AC110nv (i7-6500U/6GB/1TB/RadeonNotebook15.66.0Windows 102.19764.00Standard1366.0768.0NoNoNoIntel2.5Core i7 6500U1024.00.0HDDNoAMDRadeon R5 M330
1274AsusX553SA-XX031T (N3050/4GB/500GB/W10)Notebook15.64.0Windows 102.20369.00Standard1366.0768.0NoNoNoIntel1.6Celeron Dual Core N3050500.00.0HDDNoIntelHD Graphics